4 accepted papers
Multiple Kernel k-means and its variants integrate a group of kernels to improve clustering performance, but it still has some drawbacks: 1) linearly combining base kernels to get the optimal one limits the kernel representability and cuts off the negotiation of kernel learning and clustering; 2) ig…
This paper proposes an Efficient Multi-view Graph Clustering with Comprehensive Fusion (EMGC²F) model and a corresponding efficient optimization algorithm to address multi-view graph clustering tasks effectively and efficiently. Compared to existing works, our proposals have the following highlights…
Multiple kernel k-means clustering (MKKM) and its variants have been thoroughly studied over the past decades. However, most existing models utilize a spectrum-based two-step approach to solve the clustering objective, which may deviate from the final cluster labels and lead to suboptimal performanc…
The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtai…